Abstract:

【Objective】 The objective of this study is to assess the suitability of image process techniques for measuring and quantifying ear traits in maize DUS tests. 【Method】 The 7 ear traits, namely ear length and width, kernel top and ear axis color, ear shape, ear row number, kernel arrangement, as ruled in The National Guidelines for Maize, were measured by image processes from 50 ears each of four cultivars or 93 up to 107 ear axes each of eight cultivars. Measurements obtained were subjected to different statistical procedures in order to determine adequate data analysis requirements, in the context that the variety evaluation of distinctness. 【Result】 Relative measurement errors were 6.2%, 1.6% and 0.66% respectively for ear length, ear width and ear row number by image process. Individual ear edge angles, as a trait depicting ear shape, ranged from 0 to 2.22 degrees, and mean kernel row angles, as a trait depicting kernel arrangement, varied from 89.4-90.7 degrees among cultivars. Colors and shapes of ears, which, as usual, are quality traits or pseudo-quality traits, were quantified as quantitative traits so that information gain increased. Variations in kernel top color among both ears and sides within ears were tiny, and variations in other traits among sides within ears were smaller than or comparative to among ears. The increased number of traits based on same bulk samples may lead to higher risk of false between-varieties distinctness. Nevertheless, the risk of this kind can substantially be reduced by the mean separation for individual traits with multiple traits adjustment. 【Conclusion】 Image process is a useful tool for gathering and quantifying maize ear DUS and other more traits with advantages of objectivity, efficiency and low cost, when integrated with adequate statistical analysis tools such as the mean separation for individual traits with multiple traits adjustment, and will play more and more important roles in the new maize variety DUS testing in the whole country.

Key words: maize, ear trait, image process, DUS testing, statistical analysis